Landscape moderation of biodiversity patterns and processes ‐ eight hypotheses
Bibliographic record
Abstract
Understanding how landscape characteristics affect biodiversity patterns and ecological processes at local and landscape scales is critical for mitigating effects of global environmental change. In this review, we use knowledge gained from human-modified landscapes to suggest eight hypotheses, which we hope will encourage more systematic research on the role of landscape composition and configuration in determining the structure of ecological communities, ecosystem functioning and services. We organize the eight hypotheses under four overarching themes. Section A: 'landscape moderation of biodiversity patterns' includes (1) the landscape species pool hypothesis-the size of the landscape-wide species pool moderates local (alpha) biodiversity, and (2) the dominance of beta diversity hypothesis-landscape-moderated dissimilarity of local communities determines landscape-wide biodiversity and overrides negative local effects of habitat fragmentation on biodiversity. Section B: 'landscape moderation of population dynamics' includes (3) the cross-habitat spillover hypothesis-landscape-moderated spillover of energy, resources and organisms across habitats, including between managed and natural ecosystems, influences landscape-wide community structure and associated processes and (4) the landscape-moderated concentration and dilution hypothesis-spatial and temporal changes in landscape composition can cause transient concentration or dilution of populations with functional consequences. Section C: 'landscape moderation of functional trait selection' includes (5) the landscape-moderated functional trait selection hypothesis-landscape moderation of species trait selection shapes the functional role and trajectory of community assembly, and (6) the landscape-moderated insurance hypothesis-landscape complexity provides spatial and temporal insurance, i.e. high resilience and stability of ecological processes in changing environments. Section D: 'landscape constraints on conservation management' includes (7) the intermediate landscape-complexity hypothesis-landscape-moderated effectiveness of local conservation management is highest in structurally simple, rather than in cleared (i.e. extremely simplified) or in complex landscapes, and (8) the landscape-moderated biodiversity versus ecosystem service management hypothesis-landscape-moderated biodiversity conservation to optimize functional diversity and related ecosystem services will not protect endangered species. Shifting our research focus from local to landscape-moderated effects on biodiversity will be critical to developing solutions for future biodiversity and ecosystem service management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".